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Top1. Introduction
Organizations all over the world usually execute a large number of projects at the same time, where many of which are work-intensive with high dependence on human resources (Hartmann and Briskorn, 2010). In such organizations, human skill is an important factor because projects differ in resource consumption and skill sets. During the projects’ planning stage, engineers usually face a critical challenge of managing a pool of skillful human resources, who are responsible of executing multiple projects under strict time and cost constraints.
Software and new product/service developments are considered work-intensive projects, which impose the biggest challenges for enterprise project management offices. The main challenge is adaptation to the increasing external factors affecting the project schedule; such as, time to market and competition (Lenarduzzi and Taibi, 2016). Another challenge is to meet agreed on project delivery dates, when a project fails to meet the original schedule plan, the organization would endure additional costs reference to the budget baseline. Globally, only 31% of organizations are likely to deliver projects on time and only 29% are likely to deliver projects on budget (Heimerl and Kolisch, 2010). To deal with such challenges and achieve organization goals, there is an immense need to develop optimization models for scheduling and sequencing tasks in work-intensive multiple projects. Furthermore, work-intensive projects may face an additional problem due to occurrence of unexpected events; such as, adding a new task, a new project, drop of some project tasks. This research, therefore, develops optimization models for scheduling and sequencing projects’ tasks under unexpected events.
For normal conditions, the scheduling optimization model aims to minimize the sum of totals of undertime and overtime, and maximizing the sum of satisfactions on tasks due dates and standard processing times. Whereas, the sequencing optimization model seeks maximizing the total actual task durations and the sum of satisfactions on project delivery. The proposed optimization models may provide substantial assistance to project managers in planning multiple projects across the whole organization and maximizing the perceived benefits across the consolidated portfolio under normal and unexpected events. Furthermore, the models may assist project managers in achieving organization goals and mitigating the risk while delivering projects on time. The remaining of this paper including the introduction is outlined in the following sequence. Section 2 reviews relevant literature on project scheduling and sequencing. Section 3 develops two optimization models for scheduling and sequencing of project tasks. Section 4 provides illustrative case study. Section 5 conducts optimal scheduling and sequencing under unexpected events. Finally, section 6 summarizes research conclusions.